Recent advancements in mobile computing (i.e., smart phone technologies) and physiological sensors have led to the development of miniaturized, wearable and easily attachable sensors, which can collect, process, and transmit data to a smart phone or other mobile devices and if necessary, send the data and/or alarms to healthcare professionals using wireless communication, specialized computer networks or the Internet. The mobile devices can also display the results of testing and send feedback (e.g., diagnosis and/or recommendations) from healthcare providers. Common examples of such systems include mobile (body-worn or attached) systems for monitoring vital signs, including heart rate, respiration, blood pressure, oxygen saturation, glucose and other physiological data, psychophysiological questionnaires and behavioral assessment programs. Such systems have been used by lay public (consumers) for personal health tracking and fitness training (e.g., exercise level, weight, diet, glucose level, etc.), as well as by healthcare providers for remote management of patients with chronic diseases (telemedicine). In the telemedicine applications, patients located at home are monitored by healthcare professionals remotely, from a hospital, clinic or monitoring facility. Some other examples of mobile systems include those utilized by emergency medical services (EMS) for monitoring and transmitting vital-sign data from the patient's home or ambulance to the healthcare professionals located at healthcare clinics, offices, hospitals or monitoring facilities.
Initially, mobile systems described above used computer networks and the Internet primarily for data transmission and storage. However, manual or semi-automatic processing and analyzing large amounts of continuously collected physiological data is time consuming and labor intensive, which creates a significant burden for the healthcare professionals (physicians, nurses, technicians) and leads to the information overload, errors and processing delays. Recently, such data-intensive problems, which are collectively referred to as the “big-data” problem, have been cited by the Office of Science and Technology as one of the most important challenges facing the Nation and its healthcare (“Big Data Research and Development Initiative”; Office of Science and Technology Policy, Mar. 29, 2012). This invention addresses several important types of Big Data; specifically, physiological, psychological, and behavioral data continuously or repeatedly collected over time, also referred to as the serial analysis or time-series analysis. One challenge of such analysis is related to the need to process large amounts of data collected over time (e.g., continuous electrocardiographic data, physical activity, blood pressure, blood glucose level, etc.) in the presence of various confounders, including changing environmental conditions, physical activity, psychological status, sleep/wake cycle, and so on. Another challenge is related to the necessity to combine information obtained from different sources (also referred to as the data fusion). For example, episodes of fainting (syncope) could be detected from the continuous tracking of physical activity and body position, whereas electrocardiogram and blood pressure can be useful for discerning underlying physiological mechanisms and triggers of fainting (e.g., sudden drop in blood pressure or cardiac arrhythmia). The analysis, however, is usually obscured by a number of confounders, including ambient noise, environmental factors, physiological and psychological activity. Combining (fusing) different types of information, such as physiological, behavioral, molecular, genomics and proteomics data, which have different time scales, resolutions and analysis rules, further increases the complexity of this problem.
Clearly, the solution of Big-Data problem lies in the utilization of network-distributed computing/processing (the Internet-cloud or cloud computing) and its resources. In particular, these resources can be used for processing vast amounts of continuously collected physiological data (electrocardiogram, blood pressure, glucose, etc.), as well as genetic, biochemical and molecular data. However, the development and implementation of such mobile, network-distributed systems are not trivial due to multiple requirements and constraints. In particular, 1) the data have to be processed accurately and quickly (either in real time or with minimal delays) on mobile systems with limited computational resources; 2) large amounts of data need to be transmitted to a network cloud or some other remote location over the network (e.g., healthcare institution) using wireless communication, which often has a limited or varying speed, connection quality and may experience delays or connection failures, and 3) the time-sensitive information (e.g., life-threatening abnormalities, vital signs, test responses) or trends (e.g., trends in blood pressure, physical activity, glucose level, cholesterol, weight, etc.) need to be presented in a short, clear and user-specific formats for healthcare professionals and individual consumers.
One commonly used approach is to perform data analysis at a single location (either at the user's location or on the cloud). For example, mobile devices can collect biomedical data (e.g., blood pressure, ECG, glucose level, weight, physical activity, body position, respiration, sleep duration/quality, behavioral and psychological status) and send it to a smart phone for processing and analysis (e.g., calculation of heart rate, calories, number of steps and other parameters). However, processing data locally, using a small processor with limited computational resources, while also performing several other concurrent functions (e.g., real-time data acquisition, formatting and display), usually requires simplified signal and/or image processing and may lead to inaccuracies in data analysis and/or display, as well processing time delays. Although this might be appropriate for some simple data types (e.g., number of steps or calories burned), many biomedical signals require advanced signal processing, pattern recognition and computational algorithms, which cannot be easily simplified. For example, accurate analysis of continuous electrocardiographic (ECG) signals usually requires filtering, advanced classification algorithms and pattern recognition methods. Although small microcontrollers with limited computational resources have been utilized for ECG processing in the implantable cardiac devices and ambulatory loop recorders, the results are usually substantially less accurate (misclassified cardiac beats and arrhythmias) compared to the analysis performed on a powerful computer utilizing advanced signal processing and pattern recognition algorithms.
Another commonly used approach is to collect the data on a mobile device (smart phone) or computer; send it to the Internet cloud (or network server) for analysis, and then forward the results to users' mobile devices or computers. This approach has become more common with the development of high-speed mobile communication. It is often implemented using JAVA programming environment, which makes the programs compatible with different types of mobile devices, computers and operating systems. In this approach, the user only has a simple viewer program, which receives and displays the data (e.g., ECG waveforms) and results of analysis performed on the cloud.
However, this approach also has drawbacks. One problem is related to the limited transmission speed and associated delays when large amounts of continuous, multichannel data are transmitted over a wide-area wireless network or the Internet. This problem becomes even more challenging when continuous data are transmitted from multiple users (e.g., multiple channels of continuous ECG, blood pressure, pulse oximetry and other data sampled at 1 kHz/channel), which can further increase time delays and network traffic overloads. When the data analysis is time sensitive, for example, when vital signs are analyzed by Emergency Medical Personnel in patients with chest pain, any time delay can be dangerous. To obviate this problem, U.S. Pat. No. 8,255,238 to Powell et. al. discloses a system for remote patient monitoring, which includes physiological sensor(s), a healthcare facility data processing/storage system and a remote handheld device. The system operates with the help of two graphical application program interface (API) systems. The 1st API operates in conjunction with the healthcare facility data processing and storage system; it conditions (compresses) data for streaming across a cellular network and for reception and display on a handheld device. The 2nd API operates in conjunction with the handheld device; it conditions (compresses) the data for faster display (rendering) on the handheld device.